class_docstr: Generic PyTorch-Lightning class that must be initialized with a PyTorch module.
class_docstr_tok: ["Generic","PyTorch-Lightning","class","that","must","be","initialized","with","a","PyTorch","module","."]
code: def configure_optimizers(self):
      optimizer = self.optimizer_class(self.parameters(), lr=self.lr)
      if self.one_cycle_max_lr is None:
          return optimizer
      scheduler = torch.optim.lr_scheduler.OneCycleLR(
          optimizer=optimizer,
          max_lr=self.one_cycle_max_lr,
          total_steps=self.one_cycle_total_steps,
      )
      return {
          "optimizer": optimizer,
          "lr_scheduler": scheduler,
          "monitor": "val_loss",
      }
  
file_path: DeepKE/src/deepke/relation_extraction/few_shot/lit_models/base.py
imports: import torch
  from transformers.optimization import get_linear_schedule_with_warmup
  from torch import nn
label_desc: Use image node_id 3 for calling the BaseLitModel obj's underlying member method code with example usage: obj.configure_optimizers() and returns: dict, optimizer